To solve the problems of difficult control law design,poor portability,and poor stability of traditional multi-agent formation obstacle avoidance algorithms,a multi-agent formation obstacle avoidance method based on deep reinforcement learning (DRL) is proposed.This method combines the perception ability of convolutional neural networks (CNNs) with the decision-making ability of reinforcement learning in a general form and realizes direct output control from the visual perception input of the environment to the action through an end-to-end learning method.The multi-agent system (MAS) model of the follow-leader formation method was designed with the wheelbarrow as the control object.An improved deep Q netwrok (DQN) algorithm (we improved its discount factor and learning efficiency and designed a reward value function that considers the distance relationship between the agent and the obstacle and the coordination factor between the multi-agents) was designed to achieve obstacle avoidance and collision avoidance in the process of multi-agent formation into the desired formation.The simulation results show that the proposed method achieves the expected goal of multi-agent formation obstacle avoidance and has stronger portability compared with the traditional algorithm.